Current photo geolocation processes permit geolocation in urban and well-developed areas where many unique landmarks exist and a dense amount of photographs are taken (by tourists and residents). For example, Google Goggles, available from Google Inc., allows a user to obtain information about a famous landmark using a search based on a photograph of the landmark. For instance, the user can take a photograph of the Statue of Liberty or the Eiffel Tower and through Google Goggles can search for information on these well-known and well documented landmarks. The problem with these photo geolocation technologies is that they are not effective for remote regions where uniquely identifying features and number of photographs acquired are limited.
Current photo-to-terrain alignment processes permit registration of a ground level photograph with digital elevation maps (DEMs), if the position from which the photograph was taken is already known to within some accuracy range. See, for example, L. Baboud et al., “Automatic Photo-to-Terrain Alignment for the Annotation of Mountain Pictures,” 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 41-48 (2011) (hereinafter “Baboud”). While this technology works in remote locations, it requires geospatial coordinates of the areas from which the photographs were taken. Thus, this technology does not solve the geolocation problem described above.
Thus, improved techniques for geolocating images would be desirable.